# Mitigating Metaphors: A Comprehensible Guide to Recent Nature-Inspired Algorithms

## TL;DR

This paper provides accessible descriptions of recent nature-inspired algorithms, clarifies their relationships to classical metaheuristics, and discusses future research directions to improve understanding and development in the field.

## Contribution

It offers a comprehensible guide to recent algorithms, addressing terminology issues and highlighting connections to classical metaheuristics for better understanding.

## Key findings

- Accessible descriptions of recent algorithms
- Identified commonalities with classical metaheuristics
- Discussed future research directions

## Abstract

In recent years, a plethora of new metaheuristic algorithms have explored different sources of inspiration within the biological and natural worlds. This nature-inspired approach to algorithm design has been widely criticised. A notable issue is the tendency for authors to use terminology that is derived from the domain of inspiration, rather than the broader domains of metaheuristics and optimisation. This makes it difficult to both comprehend how these algorithms work and understand their relationships to other metaheuristics. This paper attempts to address this issue, at least to some extent, by providing accessible descriptions of the most cited nature-inspired algorithms published in the last twenty years. It also discusses commonalities between these algorithms and more classical nature-inspired metaheuristics such as evolutionary algorithms and particle swarm optimisation, and finishes with a discussion of future directions for the field.